Clinical Decision Support System in Healthcare

 V. Vidhya, Professor, SIMS

    Clinical Decision support system is a latest trend of healthcare information. This technology provides physicians and healthcare providers to make clinical decisions. Robert Hayward defined “Clinical decision support systems  link health observations with health knowledge to influence health choices by clinicians for improved health care”. CDSS is a form of Artificial Intelligence in Medicine.

    A clinical support system is making an advice clinically to the patient based on the multiple patient data. As a health IT tool CDSS has an all over reach in the healthcare sector. It is applied in the healthcare sector to improve the quality, avoid medical errors and know about the patients’ health information well in advance. CDSS as a tool enables to fix up reminders for preventive care, give alert about the dangerous drug interactions and alert healthcare providers to initiate the test that patient has to undergo. It can also give a precautious sign if there has been any kind of improper diagnosis, missed or wrong dosage of medications.

CPOE – Computerized Physician Order Entry is one of the forms of CDSS where physicians automate the medications order. CPOE is a standardized format where physicians can enter the medications and it can be integrated with CDSS for enhancing the patient safety.

Applications of CDSS in hospitals:

1. A hospital in Albama has decreased its sepsis mortality rates by implementing CDSS. Real time alerts where given for new diagnosis for sepsis and remainders where given to the providers to check for the worsening vital signs among the sepsis patients
2. Mayo Clinic employs CDS that helps the nurses to deliver complete and accurate phone screenings of patients who seek appointments. The computerised software gives detail questions to the nurses so that they do not miss any information about the patient health.
3. CDSS application with head injury patients by Yale and the Mayo Clinic gives guidelines and provides information on severity of the injury.  The application explains decision treatments to patients and reducing the number of unnecessary CT scans.

Criticisms and Solution for CDSS implementation:

            This new technology is an existing workflow of clinicians is an frustrating one.  As by AMA report clinicians spend more time on entering the data in keyboard than they spend with their patients. This makes the health care industry to change its view on the CDSS as a time-consuming process rather than time saving. Anyway, this assumption can be broken down by establishing a specific team and defining a project scope. To start with the organisation which is going to adopt CDSS technology should be ready to use it in some specific clinical departments and they should set goals and priorities for the same. The organisation should be ready to adopt any change in its work flow and this readiness will be the first step in implementing CDSS. Physicians, nurses, administrators, and other technology users should be part of the discussion while implementing CDSS and in considering CDS vendors or implementation strategies. The acceptance by the clinicians that someone discussing their patient’s diagnosis and medications will be a successful implementation of the above technology.

Collecting feedback from the users of the technology is like getting stakeholder input before going for live. The impact of patient quality care, patient safety, workflow and provider / patient satisfaction after implementation of CDSS is the feedback measure of the above technology. End user feedback can help inform future efforts and make positive changes to the process. Clinical decision support tools can improve patient safety and care quality if providers accept the addition to their workflow and don’t feel frustrated by new alerts or notifications. As CDS tool continue to grow along with machine learning and AI provider may find the future of CDS to be more intuitive and less intrusive.